Joint modeling of primary and secondary action in database marketing

In this paper we discuss the issue of primary and secondary actions to direct mail offers. Primary action refers to the first responses consumers make toward a direct offer or solicitation. It might represent an order for a product, a request for a catalog or credit card, or a pledge to donate to a charity. As little money changes hands on primary actions, companies are also interested in secondary actions, i.e., bad debts, returns, or payments. A company concentrating solely on the prediction of primary actions might lose money on customers who do not ultimately pay. We present a model which jointly models primary and secondary action and incorporates the correlation between the two action probabilities. We also show how optimal selection should take place incorporating predicted primary and secondary action jointly. In an empirical study, the new joint model yields superior profits when compared to a split model assuming the independence of primary and secondary actions.

The risk function approach to profit maximizing estimation in direct mailing

When the parameters of the model describing consumers' reaction to a mailing are known, addresses for a future mailing can be selected in a profit-maximizing way. Usually, these parameters are unknown and are to be estimated. Standard estimation are based on a quadratic loss function. In the present context an alternative loss function is suggested by the mailing company's profit function. This leads to different estimators and higher expected profit.

Celebrating 40 years of panel data analysis:Past, present and future

The present special issue features a collection of papers presented at the 2017 International Panel Data Conference, hosted by the University of Macedonia in Thessaloniki, Greece. The conference marked the 40th anniversary of the inaugural International Panel Data Conference, which was held in 1977 at INSEE in Paris, under the auspices of the French National Centre for Scientific Research. As a collection, the papers appearing in this special issue of the Journal of Econometrics continue to advance the analysis of panel data, and paint a state-of-the-art picture of the field. (c) 2020 Elsevier B.V. All rights reserved

A New Estimator for Standard Errors with Few Unbalanced Clusters

In linear regression analysis, the estimator of the variance of the estimator of the regression coefficients should take into account the clustered nature of the data, if present, since using the standard textbook formula will in that case lead to a severe downward bias in the standard errors. This idea of a cluster-robust variance estimator (CRVE) generalizes to clusters the classical heteroskedasticity-robust estimator. Its justification is asymptotic in the number of clusters. Although an improvement, a considerable bias could remain when the number of clusters is low, the more so when regressors are correlated within cluster. In order to address these issues, two improved methods were proposed; one method, which we call CR2VE, was based on biased reduced linearization, while the other, CR3VE, can be seen as a jackknife estimator. The latter is unbiased under very strict conditions, in particular equal cluster size. To relax this condition, we introduce in this paper CR3VE-A, a generalization of CR3VE where the cluster size is allowed to vary freely between clusters. We illustrate the performance of CR3VE-A through simulations and we show that, especially when cluster sizes vary widely, it can outperform the other commonly used estimators

Optimal selection of households for direct marketing by joint modeling of the probability and quantity of response

We present several methods for the maximization of expected profits when households are selected from a mailing list for a direct mail campaign. The response elicited from the campaign can vary over households, as is the case with fund raising or mail order selling. The decisions taken by the household are (a) whether to respond and, in the case of response, (b) the quantity of response, e.g. the sum donated or the monetary amount of the order. We jointly model both decisions and derive a number of profit maximizing selection methods. We empirically illustrate the methods using a data set from a charitable foundation. It appears that modeling both aspects of the response yields considerably higher profits relative to selection methods that are based on solely modeling the response probability.

Unbiased estimation of the OLS covariance matrix when the errors are clustered

When data are clustered, common practice has become to do OLS and use an estimator of the covariance matrix of the OLS estimator that comes close to unbiasedness. In this paper, we derive an estimator that is unbiased when the random-effects model holds. We do the same for two more general structures. We study the usefulness of these estimators against others by simulation, the size of the t-test being the criterion. Our findings suggest that the choice of estimator hardly matters when the regressor has the same distribution over the clusters. But when the regressor is a cluster-specific treatment variable, the choice does matter and the unbiased estimator we propose for the random-effects model shows excellent performance, even when the clusters are highly unbalanced

Moment conditions for the quadratic regression model with measurement error

We consider a new estimator for the quadratic errors-in-variables model that exploits higher-order moment conditions under the assumption that the distribution of the measurement error is symmetric and free of excess kurtosis. Our approach contributes to the literature by not requiring any side information and by straightforwardly allowing for one or more error-free control variables. We propose a Wald-type statistical test, based on an auxiliary method-of-moments estimator, to verify a necessary condition for our estimator's consistency. We derive the asymptotic properties of the estimator and the statistical test and illustrate their finite-sample properties by means of a simulation study and an empirical application to existing data from the literature. Our simulations show that the method-of-moments estimator performs well in terms of bias and variance and even exhibits a certain degree of robustness to the distributional assumptions about the measurement error. In the simulation experiments where such robustness is not present, our statistical test already has high power for relatively small samples